Ink-jet or other types of printers may have occasion to make a modest adjustment to the scaling of the image it is printing. An example is an ink-jet printer that accounts for changes in paper size in response to the wetting of the paper by the ink. Here, an ink-jet printer may initially print a first color component of an image on the paper (e.g., the black component of the image). As a consequence of the wetting of the paper by the first color component's ink the paper may expand which expands the size of the printed first color component of the image.
The printer then accounts for this expansion by slightly magnifying the size of the image of the next, second color component it will print (e.g., a red color component). Typically the magnification will be less than 10% or 5% and is often as low as 2 or 3% or even less. Scaling changes of such a small magnitude are referred to as “near unity” scaling.
Prior art near unity scaling processes adjust the size of each image plane to accurately match the previously printed color plane. This provides accurate color registration between the color planes but may not provide absolute accuracy. Another technique applies the scaling to every color plane, including the first, to accurately obtain the correct image size for the final print as it exits the printer system. This operates on an absolute basis to account for each expansion and shrinkage that may occur during each step of the process. In this second implementation scaling may include “mini-fication”, where the scaling ratio is less than one.
Near unity scaling can be positive or negative. In the case of positive scaling the size of the image is made to be larger and the scaling ratio is larger than one. In the case of negative scaling the size of the image is made to be smaller and consequently the scaling ratio is less than one.
A problem with near-unity scaling, however, is that the task of adjusting the size of an image traditionally views the image “as a whole” which necessarily attempts to account for every pixel in the image. Such an approach is generally too numerically intensive to perform. Alternatively, if some form of low overhead processing approach is taken to reduce the number of computations, the image quality suffers.
FIG. 1 demonstrates a prior art “low overhead” negative scaling approach. In the approach of FIG. 1, the image is supposed to be reduced in size by a small amount. For example, the image may originally consist of 512 by 512 pixels and need to be reduced to 500×500 pixels. For simplicity, only negative scaling along the horizontal axis is depicted.
The scaling reduction is understood to be accomplished if only 12 columns of pixels are removed. As such, the image is divided into twelfths widthwise and one pixel location at the boundary between neighboring twelfths is chosen for elimination from the image. The problem is that even though the elimination of only 12 columns of pixels seems like a negligible reduction of data, for many images the removed data it is easily detectable as a distortion to the human eye. This is essentially “nearest neighbor” scaling applied in a case with a small scaling ratio where data is deleted.
A similar approach and result also exists in the case of positive scaling. Here, for instance, imagine the original image is 500×500 pixels and needs to be scaled up to 512×512 pixels. Again, the task of magnification is understood to be accomplished if only 12 columns of pixels can be added to the image. As such, the image is divided into twelfths widthwise and one pixel location at each boundary between neighboring twelfth sections is chosen as a location where a column of pixels is to be added to the image.
Here, the “nearest neighbor” approach inserts columns of pixels simply by replicating the values of one of its neighboring column of pixels (e.g., the column of pixels on the immediate left or right of the inserted column). Again, although this seems like negligible addition of data, for many images the added data is easily detectable as a distortion to the human eye.